docs: def _store_inferential_results(self,
                                     value_array,
                                     index_names,
                                     attribute_name,
                                     series_name=None,
                                     column_names=None):
          """
          Store the estimation results that relate to statistical inference, such
          as parameter estimates, standard errors, p-values, etc.
  
          Parameters
          ----------
          value_array : 1D or 2D ndarray.
              Contains the values that are to be stored on the model instance.
          index_names : list of strings.
              Contains the names that are to be displayed on the 'rows' for each
              value being stored. There should be one element for each value of
              `value_array.`
          series_name : string or None, optional.
              The name of the pandas series being created for `value_array.` This
              kwarg should be None when `value_array` is a 1D ndarray.
          attribute_name : string.
              The attribute name that will be exposed on the model instance and
              related to the passed `value_array.`
          column_names : list of strings, or None, optional.
              Same as `index_names` except that it pertains to the columns of a
              2D ndarray. When `value_array` is a 2D ndarray, There should be one
              element for each column of `value_array.` This kwarg should be None
              otherwise.
  
          Returns
          -------
          None. Stores a pandas series or dataframe on the model instance.
          """
          if len(value_array.shape) == 1:
              assert series_name is not None
              new_attribute_value = pd.Series(value_array,
                                              index=index_names,
                                              name=series_name)
          elif len(value_array.shape) == 2:
              assert column_names is not None
              new_attribute_value = pd.DataFrame(value_array,
                                                 index=index_names,
                                                 columns=column_names)
  
          setattr(self, attribute_name, new_attribute_value)
  
          return None